CN115577909A

CN115577909A - Campus comprehensive energy system scheduling method considering price type demand response and V2G

Info

Publication number: CN115577909A
Application number: CN202211110927.1A
Authority: CN
Inventors: 何川; 吕祥梅; 南璐; 刘天琪
Original assignee: Sichuan University
Current assignee: Sichuan University
Priority date: 2022-09-13
Filing date: 2022-09-13
Publication date: 2023-01-06

Abstract

The invention discloses a campus comprehensive energy system scheduling method considering price type demand response and V2G, which comprises the steps of firstly, establishing a V2G model, a combined heat and power demand response model and a carbon transaction model; then, considering electric power/natural gas/heat balance constraint, operation constraint and main network exchange power constraint, and aiming at maximizing social welfare of the park, establishing a low-carbon economic deterministic day-ahead scheduling model of the park integrated energy system; and finally, considering the uncertainty of wind power/photovoltaic output and the uncertainty of electric/thermal load, providing a two-stage robust optimization scheduling model, and solving by using a dual transformation, an extreme point method and a CCG method. Example analysis shows that the model can effectively improve the flexibility of the system, reduce carbon emission, maintain the safety of the system under various uncertain conditions and provide a low-carbon, economic and safe scheduling scheme.

Description

Campus comprehensive energy system scheduling method considering price type demand response and V2G

Technical Field

The invention belongs to the technical field of optimization operation of an integrated energy system, and particularly relates to a campus integrated energy system scheduling method considering price type demand response and V2G.

Background

In recent years, with the global energy crisis and the increase of environmental problems, the development of clean energy and the improvement of energy quality have become common knowledge in various countries. The electric power industry of countries around the world is transitioning to sustainable energy systems, and the popularity of renewable energy sources such as wind energy and solar energy is increasing. The park Integrated Energy System (IES) is the most intuitive expression form of the energy Internet, and is coupled with a plurality of energy systems, so that the energy utilization rate is improved, and the operation cost of the energy systems is reduced. IES is expected to become the key to energy development. However, with the increasing popularity of renewable energy sources, new challenges arise from the balance of supply and demand of the IES, and the uncertainty of renewable energy power generation needs to be solved. Under the background, the research on the problem of low-carbon robust economic dispatching of the park comprehensive energy system considering uncertainty is of great significance.

Uncertainty can affect capacity configuration, system cost, and operational characteristics of the IES planning and scheduling. Most scholars deal with the uncertainties of IES using stochastic optimization, but this solution does not guarantee safe operation of the system in the worst case. Although methods such as interval optimization, fuzzy optimization, and hybrid optimization are continuously proposed, the work on two-stage robust day-ahead scheduling of campus integrated energy systems is still quite limited. Meanwhile, with the continuous maturation of the demand response technology, demand response gradually becomes an effective means for improving the operating efficiency of the IES, and the low-carbon economic dispatch of the IES in which the price type combined thermoelectric demand response and the V2G (Vehicle-to-grid) participate together is worthy of further research. In addition, the low carbon economic operation of the campus complex energy system requires the combined action of various low carbon technologies and a reasonable market mechanism. However, at present, carbon emission is reduced by optimizing the coordinated operation of the carbon capture system, the cogeneration unit and the electric gas conversion equipment, and the influence of a carbon trading mechanism or the whole carbon utilization cycle on the carbon emission is not further researched.

Therefore, on the basis of the composition of the park comprehensive energy system comprising coupling equipment such as a cogeneration unit, a gas unit, an electric boiler, an electricity-to-gas converter and the like, the price type combined thermoelectric demand response and the V2G technology are introduced, the influence of a carbon transaction mechanism and the whole carbon utilization cycle on carbon emission is further researched, and the uncertainty of wind-light output and load is considered, so that the low-carbon robust economic dispatching of the park comprehensive energy system is of great significance.

Disclosure of Invention

The technical problem to be solved by the invention is to provide a campus comprehensive energy system scheduling method considering price type demand response and V2G, and the method improves the energy utilization rate and the safety of a campus basic scene by using price type combined thermoelectric demand response and V2G technology. Through the coordinated operation of the carbon capture equipment, the carbon storage equipment and the electric gas conversion equipment, a carbon utilization cycle is formed in the system, and the carbon emission of the system is reduced. The low-carbon robust economic dispatching model of the whole park comprehensive energy system can promote renewable energy power generation and low-carbon operation, and can maintain system safety and carbon emission under the worst condition.

In order to solve the technical problems, the technical scheme adopted by the invention is as follows:

a campus integrated energy system dispatching method considering price type demand response and V2G comprises the following steps:

step 1: respectively modeling price type combined thermoelectric demand response, V2G and carbon transaction, taking park energy balance constraint, operation constraint, heat storage/gas storage constraint and power exchange constraint with a main network into account, and constructing a park comprehensive energy system low-carbon economic dispatching certainty model considering the price type combined thermoelectric demand response and the V2G with the aim of maximizing social welfare;

step 2: a complete carbon utilization cycle of the park comprehensive energy system considering carbon emission, carbon capture, carbon storage, carbon transaction and carbon consumption is formed through a cogeneration unit, a gas turbine, carbon capture equipment, carbon storage equipment and electricity-to-gas equipment, and modeling is carried out on carbon flow;

and step 3: introducing two-stage robust optimization processing of uncertainty problems of park wind-solar output and electricity/heat load on the basis of a park comprehensive energy system low-carbon economic dispatching certainty model considering price type combined thermoelectric demand response and V2G, and constructing a park comprehensive energy system low-carbon robust economic dispatching model considering price type combined thermoelectric demand response and V2G;

and 4, step 4: converting the double-layer maximum and minimum subproblem of the park comprehensive energy system low-carbon robust economic dispatching model considering the price type combined thermoelectric demand response and the V2G into a single-layer maximum problem, solving a bilinear optimization problem in the single-layer maximum problem by using an extreme point method, and finally solving the park comprehensive energy system low-carbon robust economic dispatching model considering the price type combined thermoelectric demand response and the V2G by using a column and constraint generation method;

and 5: and inputting data, equipment parameters and operation parameters of the park integrated energy system, and solving the low-carbon robust economic dispatching model of the park integrated energy system considering the price type combined thermoelectric demand response and the V2G by adopting a commercial solver GUROBI to obtain a low-carbon economic robust dispatching optimization result of the park integrated energy system.

Further, the low-carbon economic dispatching certainty model of the park integrated energy system considering the price type combined heat and power demand response and the V2G in the step 1 is specifically as follows:

(1) An objective function:

in the formula: c ^dr Revenue obtained for the price-type combined heat and power demand response;

cost associated with carbon dioxide; c ^o Operating costs for the park; c ^cur Penalizing costs for wind/light curtailment; c ^loss Penalizing costs for lost loads; t is scheduling time; e represents an electrical load; h represents a thermal load; k is the number of segments;

and

respectively representing the bid prices of the demand response electric load e and the heat load h of the kth section at the time t; p _ekt Electric power, H, at time t representing the demand-responsive electric load e of the kth stage _hkt Representing the thermal power of the demand response thermal load h of the kth section at the moment t; c ^tran A carbon transaction price; d _t Representing the carbon emission quota of the park at the time t;

represents the carbon emission at time t of the park; c ^buy Represents the unit price of carbon purchased to the carbon market; c ^sell Represents a unit price of carbon sold to the carbon market;

representing the carbon purchasing amount at the time t;

representing the carbon sale amount at the time t;

and

respectively representing the electricity purchasing price and the electricity selling price of the park to a superior power grid;

and

respectively representing the power purchase rate and the power sale power of the upper-level power grid in the park;

for gas purchase price;

purchasing gas power for a park to a superior gas network; r and w are respectively indexes of the fan and the photovoltaic;

and

respectively representing the unit price punished by wind abandoning and light abandoning;

and

respectively representing the abandoned wind power and abandoned light power at the time t of the park;

and

respectively representing the punishment price of the power loss load and the punishment price of the heat loss load; v. of _et And v _ht Respectively representing a power loss load variable and a heat loss load variable;

(2) Constraint conditions are as follows:

(2.1) cost-type Combined thermal and electric demand response constraints

When the demand response bid price is less than the time-of-use electricity price, the price type can respond to the load and participate in the operation scheduling of the park; may be responsive to the load being a positive value indicating that the electrical load is shed or shifted to another operating time, and may be responsive to the load being a negative value indicating that the time obtains a load shifted from another time to increase:

in the formula:

and

respectively representing the electric load transfer-in time and the transfer-out time;

and

respectively representing the minimum transfer-in time and the minimum transfer-out time of the electrical load; y is _et And Y _e,t-1 Respectively representing the electric load at time t and time t-1Transferring a variable 0-1 of the state, wherein the output is 1 and the input is 0; p _et Representing the actual electrical load power of the campus;

representing a predicted electrical load power; p is _ekt Representing the electric power of the electric load at the kth segment t;

indicating a responsive electrical load;

represents the kth section maximum electric power; m is a sufficiently large positive number; alpha is alpha _et Indicating a responsive electrical load ratio; (ii) a

Representing the maximum electrical load power at time t;

representing the overall amount of the electric load;

and

respectively representing the heat load transfer-in time and the transfer-out time;

and

respectively representing the minimum transfer-in time and the transfer-out time of the heat load; y is _ht And Y _h,t-1 Respectively representing the 0-1 variable of the thermal load transfer state at the time t and the time t-1, wherein the transition is 1 and the transition is 0; h _ht Representing the actual thermal load power of the campus; (ii) a

Representing a predicted thermal load power; h _hkt Representing the thermal power of the thermal load at the kth period t;

indicating a responsive thermal load;

represents the kth section maximum heating power; alpha is alpha _ht Indicating a responsive thermal load ratio;

represents the maximum thermal load power at time t;

indicating the overall amount of thermal load reduction;

(2.2) V2G constraint

In the formula: l is an index of the electric automobile;

representing the charging state of the electric automobile, wherein the charging is 1, otherwise, the charging is 0;

indicating the discharge state of the electric automobile, wherein the discharge is 1, and otherwise, the discharge is 0;

is the sum of the access time and the charging and discharging time;

respectively representing the charging power and the discharging power of the electric automobile;

respectively representing rated charging efficiency and discharging power of the electric automobile;

a variable 0-1 representing the access time of the electric automobile, wherein the access time is 1, and the rest times are 0; m represents a sufficiently large positive number;

representing the state of charge of the battery of the electric automobile;

indicating initial state of charge of electric vehicle；

Representing the battery charge state of the electric vehicle at the t-1 moment;

and

respectively representing the charging efficiency and the discharging efficiency of the electric automobile;

representing the battery capacity of the electric automobile;

the method comprises the steps that the departure time of the electric automobile is represented as 1, and the rest times are represented as 0;

representing the battery charge state at the departure time of the electric vehicle;

and

respectively representing the lower and upper limits of the state of charge of the battery;

(2.3) carbon capture and carbon storage constraints

In the formula:

represents the carbon emission at time t of the park; p, q are the indexes of the CHP and the gas turbine respectively;

represents the carbon emission of the p-th CHP at the time t;

represents the carbon emission of the qth gas turbine at time t;

representing carbon emissions generated from the purchase of electricity from an upper-level grid; i is an index of the carbon capture unit;

representing the amount of carbon dioxide trapped by the ith carbon trapping unit;

and

respectively the carbon storage amount and the carbon output amount of the carbon storage equipment;

representing the carbon purchasing amount at the time t of the park; m is an index of P2G;

represents the carbon consumption of the mth P2G at time t;

representing the carbon sale amount at the time t of the park;

the carbon capture rate;

and mu ^upper Indicating the carbon emission intensity of the CHP, gas turbine and main grid, respectively;

and

respectively representing the output of the CHP and the gas turbine at the moment t;

representing the amount of carbon dioxide required to produce natural gas at unit power;

shows the electric gas conversion efficiency of the mth station P2G;

represents the power consumption of the mth station P2G at time t; l is a radical of an alcohol ^HANG Indicating a low heating value of natural gas;

representing the carbon storage amount of the carbon storage equipment;

representing the carbon storage amount of the carbon storage equipment at the time t-1; eta ^s The loss coefficient of carbon storage; c ^s,min And C ^s,max Respectively representing the minimum carbon storage amount and the maximum carbon storage amount of the carbon storage equipment; m ^in,min And M ^in,max Representing the minimum carbon storage amount and the maximum carbon storage amount of the carbon storage equipment; m ^out,min And M ^out,max The minimum carbon output and the maximum carbon output of the carbon storage equipment are obtained; m is a group of ^b,max Represents the maximum value of the carbon purchased from the park; m ^s,max Represents the maximum value of the carbon sale amount of the park;

indicating the power consumption of the ith carbon capture unit at time tPower; theta is the energy consumption of treating unit carbon dioxide;

representing the starting and stopping states of the carbon capture equipment, wherein the starting is 1, and the shutdown is 0;

represents the fixed energy consumption of the carbon capture plant;

(2.4) energy balance constraint

In the formula:

and

respectively representing the output of the r-th fan and the w-th photovoltaic at the time t; p _et Considering the actual electric load after demand response for the time t; n is an index of the electric boiler;

representing the power consumption of the nth electric boiler at the time t;

the gas power generated by the mth P2G at the time t;

and

respectively storing and releasing gas power for the gas storage device at the moment t;

and

CHP and gas power consumed by the gas turbine, respectively; eta ^heat The heat energy utilization rate of the park;

and

the heat production power of the CHP and the electric boiler respectively;

and

respectively, the thermal power stored and released by the thermal storage device.

(2.5) Power exchange constraints with Main network

In the formula: p ^in,min And P ^in,max Respectively representing the minimum and maximum electric power purchased from the main grid; p ^out,min And P ^out,max Respectively, minimum and maximum electric power for selling electricity to the main grid; g ^in,min And G ^in,max Respectively the minimum and maximum gas power for purchasing gas from the main network;

(2.6) abandon the wind-solar constraint and the loss-of-load constraint

In the formula:

and

respectively setting allowable wind abandoning proportion, light abandoning proportion, power loss load proportion and heat loss load proportion;

(2.7) operating constraints

(2.7.1) CHP operating constraints

In the formula:

and

respectively representing the heating coefficient and the flue gas recovery rate of the CHP internal bromine refrigerator;

the power generation efficiency of the CHP internal micro-combustion engine is obtained;

the heat dissipation loss rate;

and

the startup cost and shutdown cost of the CHP, respectively;

and

the single startup cost and shutdown cost of the CHP are respectively;

and

the starting-up and shutdown states of the CHP at the time t and the time t-1 are respectively, the starting-up is 1, and the shutdown is 0;

and

minimum and maximum electrical power for CHP output, respectively;

the output of the CHP at the t-1 moment is obtained;

and

the climbing rate and descending rate of CHP are respectively;

continuous startup and shutdown time of the CHP respectively;

the minimum startup time and the minimum shutdown time of the CHP are respectively;

(2.7.2) gas turbine operating constraints

In the formula: f (-) is the heat rate curve of the gas turbine;

the startup cost and shutdown cost of the CHP, respectively;

is the minimum gas turbine output;

the starting state is the starting and stopping state of the gas turbine at the moment t, the starting is 1, and the stopping is 0;

increasing the gas consumption of the gas turbine in the k section;

electric power for the kth section of the gas turbine at time t;

(2.7.3) P2G operational constraints

In the formula:

electric gas transfer efficiency of P2G;

and

the minimum gas making power and the maximum gas making power of P2G are respectively;

(2.7.4) electric boiler operational constraints

In the formula:

the electric heating efficiency of the electric boiler;

respectively the minimum heating power and the maximum heating power of the electric boiler;

(2.7.5) operating constraints for gas storage and heat storage devices

In the formula:

and

respectively the gas storage power and the gas release power of the gas storage equipment; g ^GS,in,max And G ^GS，out,max The maximum gas storage power and the maximum gas discharge power of the gas storage device are respectively;

and

the gas storage capacities of the gas storage device at the time t and the time t-1 are respectively set; eta ^CGS 、η ^GS,in And η ^GS,out The self-consumption rate, the gas storage efficiency and the gas release efficiency of the gas storage equipment are respectively set;

and

the heat storage power and the heat release power of the heat storage equipment are respectively; h ^HS,in,max And H ^HS,out,max The maximum heat storage power and the maximum heat release power of the heat storage equipment are respectively;

and

of the heat-storage apparatus at times t and t-1, respectivelyA heat storage capacity; eta ^CHS 、η ^HS,in And η ^HS,out The self consumption rate, the heat storage efficiency and the heat release efficiency of the heat storage equipment are respectively;

(2.8) general vector form

Writing the deterministic optimal scheduling model into a general vector form:

s.t.Ax+By+Cv≤b,x∈{0,1}

in the formula: x represents the starting and stopping states of all the units, the charging and discharging states of the electric automobile and the transition-in and transition-out state of the price type combined heat and power demand response; y represents the rest scheduling power of the system; v represents the amount of unloading;

and

is a constant coefficient vector of the objective function; A. b, C and b are the constrained constant coefficient matrix and vector, respectively.

Further, the carbon utilization cycle of the integrated park energy system considering carbon emission, carbon capture, carbon storage, carbon trading and carbon consumption in step 2 is specifically as follows:

the carbon capture equipment captures carbon dioxide generated in the operation process of the cogeneration unit and the gas turbine, the captured carbon dioxide is directly supplied to the electric gas conversion equipment to generate natural gas, and the surplus carbon dioxide is stored in the carbon storage equipment or directly traded with an external carbon market or directly discharged.

Further, the low-carbon robust economic dispatching model of the park integrated energy system considering the price type combined heat and power demand response and the V2G in the step 3 is specifically as follows:

on the basis of considering a price type demand response and a V2G campus comprehensive energy system low-carbon economic dispatching certainty model, considering a two-stage robust dispatching model of wind-solar output and load forecasting uncertainty as shown in the following formula; the method comprises the following steps that in a first stage of the model, on the basis of a scene, the optimal scheduling scheme of decision states such as optimal scheduling of a park comprehensive energy system, a charging and discharging state of an electric automobile, a price type demand response transfer state and the like is adopted, and in a second stage, on the basis of the scheduling scheme in the first stage, the park unit output, V2G, demand response load and the like are adjusted according to wind-light output fluctuation and a load real-time value so as to ensure the safe operation of the system; the maximum and minimum subproblems are used for identifying the worst scene which can cause the maximum safety out-of-limit of the park under the uncertain condition;

s.t.Ax+By≤b,x∈{0,1}

is a constant coefficient vector of the objective function; u is an uncertain variable related to wind power, photovoltaic output uncertainty and load value; f (x, y) is a function relating x to y; epsilon ^RO Indicating an allowed safety threshold; A. b, C, D, E, F, G, F, B and G are constraint constant coefficient matrixes and vectors respectively.

Furthermore, the process of solving the low-carbon robust economic dispatching model of the campus integrated energy system considering the price type combined heat and power demand response and the V2G by using the dual transformation, the extreme point method and the CCG method in the step 4 is specifically as follows:

(1) The robust scheduling main problem of the park comprehensive energy system is as follows:

the main problem objective function of robust scheduling is the social welfare of the maximized park, and the constraint conditions comprise basic scene constraint and worst scene constraint;wind power output, photovoltaic output and load actual values corresponding to worst scene

Solving the subproblems in the S-th iteration, wherein S is the total number of iterations;

Ax+By≤b,x∈{0,1}

in the formula, v _s 、z _s And

the s-th iteration values of the loss load quantity, the system continuous variable and the uncertainty variable are respectively.

(2) Identifying the sub-problem of the worst scene of the park comprehensive energy system:

the double-layer maximum and minimum subproblem is a problem of identifying a worst scene, and a scene causing the system to violate a safety specified value to the maximum extent is found, namely a specific value of an uncertain quantity in the worst scene is determined; wherein x is ^* And y ^* From the main problem, λ is the dual variable of the linear inequality constraint;

Ez+Fv+Gu≤g-Cx ^* -Dy ^* :(λ)

(3) Converting the double-layer maximum and minimum subproblem into a single-layer maximization problem by using dual transformation:

s.t.λ ^T E≤f

λ ^T F≤0

λ≤0

(4) Solving a bilinear variable product lambdau problem in the single-layer maximization problem by using an extreme point method:

λ＝λ ⁰ +λ ⁺ +λ ^-

β ⁰ +β ⁺ +β ^- ＝1

-β ⁰ M≤λ ⁰ ≤β ⁰ M

-β ⁺ M≤λ ⁺ ≤β ⁺ M

-β ^- M≤λ ^- ≤β ^- M

in the formula: lambda ⁰ ，λ ⁺ And λ ^- To assist with a continuous variable, beta ⁰ ，β ⁺ And beta ^- For assisting the 0-1 variable, the corresponding u takes the upper limit u of the uncertain set ⁺ Mean value u ^b Lower limit u ^- The case (1); m is a very large number;

(5) The CCG method solves the specific flow of the proposed park comprehensive energy system low-carbon robust economic dispatching model considering the price type demand response and the V2G:

step a: let the iteration counter s =0 set the maximum value epsilon allowed by the system for violating the safety regulations ^RO ；

Step b: solving the main problem, if the main problem is solved, obtaining decision states x such as the starting and stopping states of the system unit and the output arrangement y of the unit, and performing the step c; otherwise, stopping iteration and outputting no solution;

step c: solving the maximum and minimum subproblems according to the x and y obtained by solving in the step b, and finding out the magnitude and the load value of the wind and light output under the worst scene which causes the maximum possibility of violating the safety specified value;

step d: if the maximum possible violation safety provision found in step c is less than ε ^RO Then x and y are the final optimization solution and the iteration is stopped; otherwise, let s = s +1, wind power, photovoltaic output value and load value under the worst scene solved in step c

Adding CCG constraint shown as the following formula into the main problem, and returning to the step b;

f ^T v _s ≤ε ^RO

in the formula, v _s 、z _s Respectively the s-th iteration value of the loss load quantity and the system continuous variable.

Furthermore, the data of the park integrated energy system in step 5 further includes the specific composition of the park integrated energy system and the electricity-gas-heat energy flow topology, the equipment parameters of the park integrated energy system include the number, capacity and upper and lower limits of output/charge/discharge power of a fan, a photovoltaic cell, a cogeneration unit, a gas turbine, an electric boiler, a P2G, a gas storage device, a heat storage device, an electric vehicle, a carbon capture device and a carbon storage device, and the operation parameters of the park integrated energy system include the electricity purchase price to a superior grid, the gas purchase price to the superior grid, the carbon transaction price, and various operation parameters, price type combined heat and electricity demand response proportion and electric heat load prediction data of the above equipment.

Compared with the prior art, the invention has the beneficial effects that:

1) Under the condition of considering V2G technology and price type combined heat and power demand response, a two-stage robust scheduling model of the park comprehensive energy system is established. By adaptively adjusting the charging/discharging of the electric vehicle and shifting the peak time electrical/thermal load to the off-peak time by the price-type combined heat and power demand response, the proposed robust model can improve the system operating efficiency in the basic situation while ensuring the system safety in the presence of uncertainty.

2) Detailed modeling of the carbon flow of the park integrated energy system, in which carbon emissions, carbon capture, carbon storage, carbon trading, and carbon consumption through various equipment are considered, forms a complete carbon utilization cycle. In addition, considering carbon trading mechanisms that may be penalized greatly when carbon emission quotas are exceeded, the two-stage robust scheduling model may also keep the carbon emissions of the campus within an acceptable range under low wind and photovoltaic power generation.

3) The V2G technology can effectively prevent the electric automobile from being charged in the peak period, thereby reducing the peak-valley difference, relieving the system operation pressure and reducing the operation cost of the park comprehensive energy system; the price type combined heat and power demand response can enhance the flexibility of the system, obviously reduce the electricity purchasing cost and promote the permeability of renewable energy sources; the carbon emission right trading guide system actively adopts a clean production mode to maintain load balance. Through sensitivity analysis on carbon emission right trading, a reasonable pricing mechanism is proved to be capable of remarkably reducing carbon emission.

Drawings

FIG. 1 is a flow chart of the steps of the method of the present invention.

Fig. 2 is a graph visually showing the change of price response load with respect to the change of electricity price in the demand response ladder type price curve.

FIG. 3 is a schematic diagram of a carbon utilization cycle of a park integrated energy system.

Fig. 4 is a detailed composition diagram of the park integrated energy system.

Figure 5 is a net electrical load diagram under the campus integrated energy system economic dispatch without considering the carbon trading mechanism.

Figure 6 is a heat load diagram under the campus integrated energy system economic dispatch without considering the carbon trading mechanism.

Fig. 7 shows the variation of the power purchased, the cogeneration unit output, the gas turbine output and the power sold under the low-carbon robust economic dispatch of the park integrated energy system considering different carbon transaction prices.

Detailed Description

In order to explain the technical solutions disclosed in the present invention in detail, the present invention will be further described with reference to the accompanying drawings and specific examples.

The invention discloses a low-carbon robust economic dispatching operation method of a park comprehensive energy system considering price type demand response and V2G. The specific implementation step flow is shown in fig. 1, and the technical scheme of the invention comprises the following steps:

step 1: modeling is respectively carried out on the price type combined thermoelectric demand response, the V2G and the carbon transaction, the park energy balance constraint, the operation constraint, the heat storage/gas storage constraint and the main network power exchange constraint are taken into consideration, and a park comprehensive energy system low-carbon economic dispatching certainty model considering the price type combined thermoelectric demand response and the V2G is constructed with the aim of maximizing social welfare.

(1.1) objective function:

in the formula: c ^dr Revenue derived for price-based combined heat and power demand responseBenefiting;

and

respectively representing the bid prices of the k-th section of the demand response electric load e/the heat load h at the time t; p _ekt And H _hkt Respectively representing the electric/thermal power of the demand response electric/thermal load of the kth section at the moment t; c ^tran A carbon transaction price; d _t Representing the carbon emission quota at time t of the park;

representing the carbon purchasing amount at the time t;

representing the carbon sale amount at the time t;

and

respectively representing the prices of electricity purchase and electricity sale from the park to a superior power grid;

and

respectively representing the power purchased and sold from the park to the superior power grid;

for gas purchase price;

the power for purchasing and selling gas to the superior gas network in the park; r and w are indexes of the fan and the photovoltaic respectively;

and

respectively representing the punishment price of power loss/heat load; v. of _et And v _ht Representing the loss load/thermal load variables, respectively.

(1.2) constraint conditions:

(1.2.1) price type combined heat and power demand response constraints

The energy consumption of the price response type load monotonously decreases with the increase of the electricity price, and the change of the price response load relative to the change of the electricity price can be visually represented by a demand response step type price curve as shown in fig. 2. The utility model is characterized in that the utility model comprises a plurality of price type response loads, wherein the price type response loads are divided or transferred to other operation time periods according to the change of the energy market price, namely, when the price of the demand response bid is smaller than the time-of-use price, the price type utility loads participate in the park operation scheduling.

In the formula:

and

respectively representing the electric load transfer-in/transfer-out time;

and

respectively representing the minimum transfer-in/transfer-out time of the electric load; y is _et A variable 0-1 representing the electric load transfer state, and the output is 1; p _et Representing the actual electrical load power of the campus; alpha is alpha _et Indicating a responsive electrical load ratio;

representing predicted electricityLoad power; p _ekt Represents the electric power of the electric load at the kth section t;

indicating a responsive electrical load;

represents the kth section maximum electric power; m is a sufficiently large positive number;

representing the maximum electrical load power at time t;

representing the overall amount of the electric load;

and

respectively representing the heat load transfer-in/transfer-out time;

and

respectively representing the minimum heat load transfer-in/transfer-out time; y is _ht A variable 0-1 representing the heat load transfer state, the roll-out being 1; h _ht Representing the actual thermal load power of the campus; alpha is alpha _ht Indicating a responsive thermal load ratio;

indicating a responsive thermal load;

represents the kth section maximum heating power;

represents the maximum thermal load power at time t;

indicating the overall amount of thermal load reduction.

(1.2.2) V2G constraint

In the formula: l is an index of the electric vehicle;

a variable 0-1 representing the access time of the electric automobile, wherein the access time is 1, and the rest times are 0;

is the sum of the access time and the charging/discharging time;

respectively representing the rated charge/discharge power of the electric automobile; m represents a sufficiently large positive number;

representing the state of charge of the battery of the electric automobile;

representing the initial charge state of the electric vehicle;

and

respectively representing the charging/discharging efficiency of the electric automobile;

the battery capacity of the electric automobile is represented;

the method comprises the steps of representing the leaving time of the electric automobile, wherein the leaving time is 1, and the rest times are 0;

and

representing the lower and upper limits of the battery state of charge, respectively.

(1.2.3) carbon capture and carbon storage constraints

In the formula: p and q are indexes of the CHP and the gas turbine respectively;

represents the carbon emission of the p-th CHP at the time t;

represents the carbon emission of the qth gas turbine at time t;

and

respectively the carbon storage/output quantity of the carbon storage equipment; m is the index of P2G;

represents the carbon consumption of the mth P2G at time t;

the carbon capture rate;

representing the amount of carbon dioxide required to produce natural gas at a unit power;

shows the electric gas conversion efficiency of the mth station P2G;

represents the power consumption of the mth station P2G at time t; l is ^HANG The low heat value of the natural gas is expressed, and the value is 9.7 kW.h/m ³ ；

Representing the carbon storage amount of the carbon storage equipment;

representing the carbon storage amount of the carbon storage equipment at the time t-1; eta ^s The loss coefficient of carbon storage; c ^s,min And C ^s,max Respectively representing the minimum/maximum carbon storage amount of the carbon storage equipment; m ^in,min And M ⁱⁿ ^,max Representing the minimum/maximum carbon storage amount of the carbon storage equipment; m is a group of ^out,min And M ^out,max Minimum/maximum carbon output for carbon storage facility; m ^b ^,max Represents the maximum value of the carbon purchased from the park; m ^s,max Represents the maximum value of the carbon sale amount of the park;

the power consumption at the t moment of the ith carbon capture unit is represented; theta is the energy consumption of treating unit carbon dioxide;

representing the fixed energy consumption of the carbon capture plant.

(1.2.4) energy balance constraints

In the formula:

and

respectively representing the output of the r-th fan and the w-th photovoltaic at the time t; p is _et Considering the actual electric load after demand response for the time t; n is an index of the electric boiler;

representing the power consumption of the nth electric boiler at the time t;

the gas power generated by the mth P2G at the time t;

respectively storing and releasing gas power for the gas storage device at time t;

the heat production power of the CHP and the electric boiler respectively;

and

(1.2.5) Power exchange constraints with Main network

In the formula: p ^in,min And P ^in,max Respectively representing the minimum and maximum electric power purchased from the main grid; p ^out,min 、P ^out,max Minimum and maximum electric power for selling electricity to the main grid, respectively; g ^in,min 、G ^in,max Respectively the minimum and maximum gas power for purchasing gas from the main network.

(1.2.6) abandon wind-solar constraint and loss-of-load constraint

In the formula:

and

the allowable wind abandoning proportion, light abandoning proportion, power loss load proportion and heat loss load proportion are respectively.

(1.2.7) operating constraints

(1.2.7.1) CHP operating constraints

In the formula:

and

the heat dissipation loss rate;

the startup and shutdown costs of the CHP, respectively;

the cost of a single startup and shutdown of the CHP respectively;

minimum and maximum electrical power for CHP output, respectively;

the output force of the CHP at the moment t-1;

the climbing rate and descending rate of CHP are respectively;

continuous startup and shutdown time of the CHP respectively;

the minimum on time and the minimum off time of the CHP, respectively.

(1.2.7.2) gas turbine operating constraints

In the formula: f (-) is the heat rate curve of the gas turbine;

the startup and shutdown costs of the CHP, respectively;

minimum gas turbine output;

increasing the gas consumption of the gas turbine in the k section;

the electric power of the gas turbine at the kth stage at the time t.

(1.2.7.3) P2G operating constraints

In the formula:

electric gas transfer efficiency of P2G;

respectively the minimum and maximum gas making power of P2G.

(1.2.7.4) electric boiler operation constraints

In the formula:

the electric heating efficiency of the electric boiler;

respectively the minimum and maximum heating power of the electric boiler.

(1.2.7.5) operation constraints of gas storage and Heat storage devices

In the formula:

respectively the storage/discharge power of the gas storage device; g ^GS,in,max 、G ^GS,out,max The maximum storage/discharge power of the gas storage equipment is respectively;

the storage/release power of the heat storage device; h ^HS,in,max 、H ^HS,out,max The maximum storage/discharge power of the heat storage device;

the heat storage capacities of the heat storage equipment at the time t and the time t-1 are respectively; eta ^CHS 、η ^HS,in And η ^HS,out Respectively, the self consumption rate, the heat storage efficiency and the heat release efficiency of the heat storage equipment.

(1.2.8) general vector form

For ease of discussion, the deterministic optimized scheduling model described above is written in the general form of a vector.

s.t.Ax+By+Cv≤b,x∈{0,1}

In the formula: x represents the starting and stopping states of each unit, the charging and discharging states of the electric automobile and the price type unionSwitching on and switching off the power demand response; y represents the remaining scheduled power of the system; v represents the amount of unloading;

and

Step 2: a complete park comprehensive energy system carbon utilization cycle considering carbon emission, carbon capture, carbon storage, carbon transaction and carbon consumption is formed through a combined heat and power generation unit, a gas turbine, carbon capture equipment, carbon storage equipment, electric gas conversion equipment and the like, and carbon flow is modeled.

The carbon utilization cycle of the park integrated energy system is shown in figure 3. The carbon capture equipment captures carbon dioxide generated in the operation process of the cogeneration unit and the gas turbine, the captured carbon dioxide is directly supplied to the electric gas conversion equipment to generate natural gas, and the surplus carbon dioxide is stored in the carbon storage equipment or directly traded with an external carbon market or directly discharged.

And 3, step 3: on the basis of considering the price type combined thermoelectric demand response and the V2G park integrated energy system low-carbon economic dispatching certainty model, the problems of park wind-light output and electricity/heat load uncertainty are processed by two-stage robust optimization, and the price type combined thermoelectric demand response and the V2G park integrated energy system low-carbon robust economic dispatching model is constructed.

On the basis of considering price type demand response and a V2G campus integrated energy system low-carbon economic dispatching deterministic model, a two-stage robust dispatching model considering uncertainty of wind-solar output and load prediction is shown as the following formula. The model is an optimal scheduling scheme of decision states such as optimal scheduling of a park comprehensive energy system, a charging and discharging state of an electric automobile, a price type demand response transfer state and the like in a scene based on a first stage, and a second stage is to adjust park unit output, V2G, demand response load and the like according to wind-light output fluctuation and a load real-time value on the basis of the scheduling scheme of the first stage so as to ensure safe operation of the system. The maximum and minimum subproblems are used to identify the worst scenario that may lead to the maximum safety violation of the campus under uncertain conditions.

s.t.Ax+By≤b,x∈{0,1}

is a constant coefficient vector of the objective function; u is an uncertain variable related to wind power, photovoltaic output uncertainty and load value; epsilon ^RO Indicating an allowed safety threshold; A. b, C, D, E, F, G, F, B and G are constraint constant coefficient matrixes and vectors respectively.

And 4, step 4: converting the double-layer maximum and minimum subproblem of the park comprehensive energy system low-carbon robust economic dispatching model considering the price type combined thermoelectric demand response and the V2G into a single-layer maximum problem by using a dual theory method, solving a bilinear optimization problem in the single-layer maximum problem by using an extreme point method, and finally solving the park comprehensive energy system low-carbon robust economic dispatching model considering the price type combined thermoelectric demand response and the V2G by using a Column and Constraint Generation (CCG) method.

(4.1) the robust scheduling main problem of the park comprehensive energy system:

the main problem objective function of robust scheduling is to maximize social welfare of the campus, and the constraint conditions comprise basic scene constraints and worst scene constraints. Wind power output, photovoltaic output and load actual values corresponding to worst scene

And (4) solving the subproblem in the S-th iteration, wherein S is the total number of iterations.

Ax+By≤b,x∈{0,1}

(4.2) identifying the sub-problem of the worst scenario of the park comprehensive energy system:

the double-layer maximum and minimum sub-problem is a problem of identifying a worst scene, and a scene causing the system to violate a safety specified value to the maximum extent is found, namely a specific value of an uncertain quantity in the worst scene is determined. Wherein x is ^* And y ^* Derived from the main problem, λ is the dual variable of the linear inequality constraint.

Ez+Fv+Gu≤g-Cx ^* -Dy ^* :(λ)

(4.3) converting the double-layer maximum-minimum subproblem into a single-layer maximization problem by using dual transformation:

s.t.λ ^T E≤f

λ ^T F≤0

λ≤0

(4.4) solving the bilinear variable product lambda u problem in the single-layer maximization problem by using an extreme point method:

λu＝λ ⁰ u ^b +λ ⁺ u ⁺ +λ ^- u ^-

λ＝λ ⁰ +λ ⁺ +λ ^-

β ⁰ +β ⁺ +β ^- ＝1

-β ⁰ M≤λ ⁰ ≤β ⁰ M

-β ⁺ M≤λ ⁺ ≤β ⁺ M

-β ^- M≤λ ^- ≤β ^- M

in the formula: lambda [ alpha ] ⁰ ，λ ⁺ And λ ^- To assist with a continuous variable, beta ⁰ ，β ⁺ And beta ^- For assisting the 0-1 variable, the corresponding u takes the upper limit u of the uncertain set ⁺ Mean value u ^b Lower limit u ^- The case (1); m is a very large number.

(4.5) solving the specific flow of the proposed park comprehensive energy system low-carbon robust economic dispatching model considering the price type demand response and the V2G by the CCG method:

The following equation is added to the main problemAnd c, returning to the step b.

f ^T v _s ≤ε ^RO

And 5: and inputting data, equipment parameters, operation parameters and the like of the park integrated energy system, and solving the low-carbon robust economic dispatching model of the park integrated energy system considering the price type combined heat and power demand response and the V2G by adopting a commercial solver GUROBI to obtain a low-carbon economic robust dispatching optimization result of the park integrated energy system.

The park integrated energy system data further comprises specific composition of a park integrated energy system and an electricity-gas-heat energy flow topology, the park integrated energy system equipment parameters comprise the number, the capacity and the upper and lower limits of output/charge-discharge power of a fan, a photovoltaic cell, a cogeneration unit, a gas turbine, an electric boiler, P2G, gas storage equipment, heat storage equipment, an electric automobile, carbon capture equipment and carbon storage equipment, and the park integrated energy system operation parameters comprise electricity purchase price to a higher-level power grid, gas purchase price to the higher-level gas grid, carbon transaction price, various operation parameters of the equipment, price type combined heat and electricity demand response proportion and electric heat load prediction data.

The effects of the present invention will be described in detail below with reference to specific examples.

(1) Introduction to the examples

The park integrated energy system composition shown in fig. 4 is subjected to low-carbon robust economic dispatch example analysis of the park integrated energy system. The park comprises 30 electric vehicles, two gas turbines and one fan, a photovoltaic cell, a cogeneration unit, an electric-to-gas device, an electric boiler, a gas storage device and a heat storage device. The test tool used Matlab2020b programming software and a GUROBI9.1 commercial solver.

(2) Description of embodiment scenarios

In order to verify the effectiveness of the low-carbon robust economic dispatching model of the park comprehensive energy system considering price type combined heat and power demand response and V2G, the following calculation examples 1-9 are set; to verify the impact of carbon emission right trade prices on campus carbon emissions, examples 10-12 were set.

Example 1: deterministic scheduling without regard to V2G and demand response;

example 2: considering deterministic scheduling of V2G;

example 3: deterministic scheduling of considering V2G and combined thermal power demand response;

example 4: considering the carbon transaction mechanism on the basis of the example 1;

example 5: considering carbon trading mechanism based on the formula 2;

example 6: considering the carbon transaction mechanism on the basis of the example 3;

example 7: robust scheduling is carried out on the basis of the embodiment 4;

example 8: carrying out robust scheduling on the basis of the example 5;

example 9: carrying out robust scheduling on the basis of the formula 6;

example 10: changing the price of the carbon emission right to be 1.2$/kg on the basis of the calculation example 9;

example 11: changing the price of the carbon emission right to be 12$/kg on the basis of the calculation example 9;

example 12: the carbon emission right price was changed to 120$/kg based on calculation example 9.

(3) EXAMPLES analysis of results

Table 1 shows the cost/benefit comparison of the campus integrated energy system low-carbon economic certainty scheduling algorithms 1-6, where the cost is a positive value and the benefit is a negative value. From this, it is possible to obtain: consideration of V2G and the combined heat and power demand response can significantly reduce the overall cost of campus operations, facilitating economic operation of the system. After the carbon trading mechanism is introduced, although the trading cost of the carbon emission right is increased, the system reduces the power purchasing from the main network with high carbon emission intensity. The combined action of V2G, combined thermal power demand response and carbon trading mechanisms promotes low-carbon economic operation of the campus complex energy systems.

TABLE 1 calculate cost/benefit ($) for examples 1-6

Figures 5 and 6 are net electrical load and thermal load diagrams, respectively, of the campus integrated energy system deterministic scheduling algorithms 1-3 without regard to carbon trading mechanisms. It can be seen that the V2G can enhance the controllability of the electric automobile, effectively avoid the electric automobile from being charged in the peak period, and improve the system safety. The flexibility of system operation can be significantly improved by the price type combined heat and power demand response, and the 'peak load regulation and valley load filling' of the park is realized.

Table 2 shows the carbon capture and carbon emissions for examples 1-6, which are readily obtained: when considering the carbon trading mechanism, if the total carbon emissions falls below the emission quota, the remaining quota may be sold. Conversely, when the emission amount is greater than the emission allowance, the emission allowance must be purchased from other units, otherwise, the emission will not be allowed. In other words, the introduction of carbon capture equipment and carbon trading mechanisms can greatly reduce the carbon emissions from the campus energy complex.

TABLE 2 carbon capture and carbon emissions (kg) for examples 1-6

Table 3 shows the total cost and carbon emissions for comparative examples 4-9, as can be seen: the robust scheduling sacrifices the economy and low carbon to a certain extent so as to deal with uncertainty and ensure the safe operation of the system. Moreover, the carbon emission of the worst scene of the robust scheduling is equal to that of the basic scene, and both are within an acceptable range.

TABLE 3 comparison of Total cost and carbon emissions for examples 4-9

Fig. 7 is a diagram showing the variation of the power purchased, the cogeneration unit output, the gas turbine output, and the power sold under the low-carbon robust economic dispatch of the campus integrated energy system considering different carbon trading prices. As can be seen from fig. 7, as the carbon emission right trade price increases, the campus integrated energy system gradually shifts from economic operation to minimum carbon emission optimization. Justified pricing mechanisms can significantly reduce carbon emissions.

The above description is only an embodiment of the present invention, but not intended to limit the scope of the present invention, and all equivalent changes or substitutions made by using the contents of the present specification and the drawings, which are directly or indirectly applied to other related fields, should be included in the scope of the present invention.

Claims

1. A campus integrated energy system dispatching method considering price type demand response and V2G is characterized by comprising the following steps:

step 1: modeling the price type combined thermoelectric demand response, the V2G and the carbon transaction respectively, taking the park energy balance constraint, the operation constraint, the heat storage/gas storage constraint and the main network power exchange constraint into consideration, and constructing a park comprehensive energy system low-carbon economic dispatching deterministic model taking the price type combined thermoelectric demand response and the V2G into consideration with the maximized social welfare as a target;

and 3, step 3: introducing two-stage robust optimization processing of uncertainty problems of park wind-solar output and electricity/heat load on the basis of a park comprehensive energy system low-carbon economic dispatching certainty model considering price type combined thermoelectric demand response and V2G, and constructing a park comprehensive energy system low-carbon robust economic dispatching model considering price type combined thermoelectric demand response and V2G;

2. The campus integrated energy system dispatching method considering price type demand response and V2G as claimed in claim 1, wherein the deterministic model of low-carbon economic dispatching of campus integrated energy system considering price type combined heat and power demand response and V2G in step 1 is specifically as follows:

(1) An objective function:

and

respectively representing the bid prices of the demand response electric load e and the heat load h of the kth section at the time t; p _ekt Electric power, H, representing the demand-responsive electric load e of the k-th stage at time t _hkt Representing the thermal power of the demand response thermal load h of the kth section at the moment t; c ^tran Trading prices for carbon; d _t Representing the carbon emission quota at time t of the park;

represents the carbon emission at time t of the park; c ^buy Represents the unit price of carbon purchased to the carbon market; c ^sell Represents the unit price of carbon sold to the carbon market;

representing the carbon purchasing amount at the time t;

representing the carbon sale amount at the time t;

and

respectively representing the electricity purchasing price and the electricity selling price of the park to a superior power grid; p _t ⁱⁿ And P _t ^out Respectively representing the power purchase rate and the power sale rate of the upper-level power grid in the park;

for gas purchase price;

and

respectively representing unit prices of wind abandonment and light abandonment punishment;

and

respectively representing abandoned wind power and abandoned light power at t moment of the park;

and

(2) Constraint conditions are as follows:

(2.1) cost-type Combined thermal and electric demand response constraints

in the formula:

and

respectively representing the electric load transfer-in time and the transfer-out time; t is a unit of _e ^on And T _e ^off Respectively representing the minimum transfer-in time and the minimum transfer-out time of the electrical load; y is _et And Y _e,t-1 Respectively representing 0-1 variables of the electric load transfer state at the time t and the time t-1, wherein the output is 1 and the input is 0; p _et Representing the actual electrical load power of the campus;

representing a predicted electrical load power; p _ekt Representing the electric power of the electric load at the kth segment t;

indicating a responsive electrical load;

represents the kth section maximum electric power; m is a sufficiently large positive number; alpha is alpha _et Indicating a responsive electrical load ratio;

representing the maximum electrical load power at time t;

representing the overall amount of the electric load;

and

and

respectively representing the minimum transfer-in time and the transfer-out time of the heat load; y is _ht And Y _h,t-1 Respectively representing the 0-1 variable of the thermal load transfer state at the time t and the time t-1, wherein the transition is 1 and the transition is 0; h _ht Representing the actual thermal load power of the campus;

indicating predicted thermal negativityThe charge power; h _hkt Representing the thermal power of the thermal load at the kth period t;

indicating a responsive thermal load;

represents the maximum thermal load power at time t;

indicating the overall heat load reduction;

(2.2) V2G constraint

In the formula: l is an index of the electric automobile;

is the sum of the access time and the charging and discharging time;

respectively representing the charging power and the discharging power of the electric automobile; p _l ^c,rate 、P _l ^d,rate Respectively representing the rated charging efficiency and the rated discharging power of the electric automobile;

representing the state of charge of the battery of the electric automobile;

representing the initial charge state of the electric vehicle;

representing the battery charge state of the electric vehicle at the t-1 moment; eta _l ^ev,c And η _l ^ev,d Respectively representing the charging efficiency and the discharging efficiency of the electric automobile;

the battery capacity of the electric automobile is represented;

and

respectively representing the lower and upper limits of the battery state of charge;

(2.3) carbon capture and carbon storage constraints

In the formula:

representing the carbon emission of the park at the time t; p, q are the indexes of the CHP and the gas turbine respectively;

represents the carbon emission of the p-th CHP at the time t;

denotes the q-th gas turbine at _t The carbon emissions at the moment;

and

represents the carbon consumption of the mth P2G at time t;

representing the carbon sale amount at the time t of the park;

the carbon capture rate;

and

shows the electric gas conversion efficiency of the mth station P2G;

represents the power consumption of the mth station P2G at time t; l is ^HANG Indicating a low heating value of natural gas;

representing the carbon storage amount of the carbon storage equipment;

the carbon storage amount of the carbon storage equipment at the time t-1 is represented; eta ^s The loss coefficient of carbon storage; c ^s,min And C ^s,max Respectively representing the minimum carbon storage amount and the maximum carbon storage amount of the carbon storage equipment; m is a group of ^in,min And M ^in,max Represents the minimum carbon deposit amount of the carbon storage equipment andmaximum carbon deposit; m ^out,min And M ^out,max The minimum carbon output and the maximum carbon output of the carbon storage equipment are obtained; m ^b,max Represents the maximum value of the carbon purchased from the park; m ^s,max Represents the maximum value of the carbon sale amount of the park;

the power consumption at the t moment of the ith carbon capture unit is shown; theta is the energy consumption of treating unit carbon dioxide;

represents the fixed energy consumption of the carbon capture plant;

(2.4) energy balance constraints

In the formula:

and

representing the power consumption of the nth electric boiler at the time t;

the gas power generated by the mth P2G at the time t;

and

and

and

the CHP and the heat production power of the electric boiler are respectively;

and

respectively storing and releasing thermal power for the heat storage equipment;

(2.5) Power exchange constraints with Main network

P ^in,min ≤P _t ⁱⁿ ≤P ^in,max

P ^out,min ≤P _t ^out ≤P ^out,max

(2.6) abandon the wind-solar constraint and the loss-of-load constraint

In the formula:

and

respectively an allowable wind abandoning proportion, an allowable light abandoning proportion, an allowable power loss load proportion and an allowable heat loss load proportion;

(2.7) operating constraints

(2.7.1) CHP operating constraints

In the formula:

and

the heat dissipation loss rate;

and

the startup cost and shutdown cost of the CHP, respectively;

and

the single startup cost and shutdown cost of the CHP are respectively;

and

and

minimum and maximum electrical power for CHP output, respectively;

is CHP atthe output at the time of t-1;

and

the climbing rate and descending rate of CHP are respectively;

continuous startup and shutdown time of the CHP respectively;

(2.7.2) gas turbine operating constraints

In the formula: f (-) is the heat rate curve of the gas turbine;

the startup cost and shutdown cost of the CHP, respectively;

is the minimum gas turbine output;

increasing the gas consumption of the gas turbine in the k section;

electric power for the kth section of the gas turbine at time t;

(2.7.3) P2G operational constraints

In the formula:

the electric gas conversion efficiency of P2G is obtained;

and

(2.7.4) electric boiler operational constraints

In the formula:

the electric heating efficiency of the electric boiler;

(2.7.5) operating constraints for gas storage and heat storage devices

In the formula:

and

the gas storage power and the gas discharge power of the gas storage device are respectively; g ^GS,in,max And G ^GS,out,max The maximum gas storage power and the maximum gas discharge power of the gas storage device are respectively;

and

and

and

the heat storage capacities of the heat storage equipment at the time t and the time t-1 are respectively; eta ^CHS 、η ^HS,in And η ^HS,out The self consumption rate, the heat storage efficiency and the heat release efficiency of the heat storage equipment are respectively;

(2.8) general vector form

Writing the deterministic optimal scheduling model into a general vector form:

s.t.Ax+By+Cv≤b,x∈{0,1}

and

3. The method for scheduling the integrated energy system for the park according to claim 1, wherein the carbon utilization cycle of the complete park integrated energy system considering carbon emission, carbon capture, carbon storage, carbon trading and carbon consumption in step 2 is specifically as follows:

4. The campus integrated energy system dispatching method considering price type demand response and V2G as claimed in claim 1, wherein the campus integrated energy system low-carbon robust economic dispatching model considering price type combined heat and power demand response and V2G in step 3 is as follows:

s.t.Ax+By≤b,x∈{0,1}

is a constant coefficient vector of the objective function; u is an uncertain variable related to wind power, photovoltaic output uncertainty and load value; f (x, y) is a function of x and y and the correlation; epsilon ^RO Indicating an allowed safety threshold; A. b, C, D, E, F, G, F, B and G are constraint constant coefficient matrixes and vectors respectively.

5. The scheduling method of the campus integrated energy system considering the price type demand response and the V2G according to claim 1, wherein the solving process of the campus integrated energy system considering the price type combined thermoelectric demand response and the V2G low-carbon robust economic scheduling model by using the dual transformation, the extreme point method and the CCG method in step 4 is specifically as follows:

robust scheduling with a master problem objective function of maximizing park communityWelfare, constraint conditions include a base scenario constraint and a worst scenario constraint; wind power output, photovoltaic output and load actual values corresponding to worst scene

Solving the subproblems in the S-th iteration to obtain the result, wherein S is the total number of iterations;

Ax+By≤b,x∈{0,1}

in the formula, v _s 、z _s And

respectively obtaining the s-th iteration values of the loss load quantity, the system continuous variable and the uncertainty variable;

Ez+Fv+Gu≤g-Cx ^* -Dy ^* :(λ)

(3) Converting the double-layer maximum-minimum sub-problem into a single-layer maximization problem by using dual transformation:

s.t.λ ^T E≤f

λ ^T F≤0

λ≤0

(4) Solving the problem of bilinear variable product lambdau in the single-layer maximization problem by using an extreme point method:

λ _u ＝λ ⁰ u ^b +λ ⁺ u ⁺ +λ ^- u ^-

λ＝λ ⁰ +λ ⁺ +λ ^-

β ⁰ +β ⁺ +β ^- ＝1

-β ⁰ M≤λ ⁰ ≤β ⁰ M

-β ⁺ M≤λ ⁺ ≤β ⁺ M

-β-M≤λ ^- ≤β ^- M

in the formula: lambda [ alpha ] ⁰ ，λ ⁺ And λ ^- To assist with a continuous variable, beta ⁰ ，β ⁺ And beta ^- For assisting the 0-1 variable, the corresponding u takes the upper limit u of the uncertain set ⁺ Mean value u ^b Lower limit u ^- (ii) the condition of (a); m is a very large number;

(5) The CCG method solves the specific flow of the proposed campus comprehensive energy system low-carbon robust economic dispatching model considering price type demand response and V2G:

step a: let iteration counter s =0 set the maximum value epsilon of security violation allowed by the system ^RO ；

f ^T v _s ≤ε ^RO

in the formula, v _s 、z _s The s-th iteration value of the loss load quantity and the system continuous variable respectively.

6. The scheduling method of a park integrated energy system considering price type demand response and V2G according to claim 1, wherein the park integrated energy system data of step 5 further includes park integrated energy system specific composition and electricity-gas-heat energy flow topology, the park integrated energy system device parameters include the number, capacity and output/charge/discharge power upper and lower limits of a fan, a photovoltaic cell, a cogeneration unit, a gas turbine, an electric boiler, P2G, a gas storage device, a heat storage device, an electric vehicle, a carbon capture device and a carbon storage device, the park integrated energy system operation parameters include an electricity purchase price to a higher-level power grid, a gas purchase price to a higher-level gas grid, a carbon transaction price and various operation parameters of the above devices, a price type combined heat and power demand response ratio and electric heat load prediction data.